Re: Del Giudice F, Santarelli V, Spurna K, et al. 10-yr Outcomes of Senior Clinical Fellowship in Robot-assisted Radical Cystectomy (RARC) with focus on Intracorporeal Ileal Conduit Urinary Diversion: Aggregated Learning Curve Assessment and Achievement of the EAU-Robotic Urology Section (ERUS) RARC Curriculum. Eur Urol Open Sci 2026;89:12–22

Dear Editor,

We read with great interest the study by Del Giudice and colleagues evaluating a decade of robot-assisted radical cystectomy (RARC) training within a structured senior fellowship program and examining learning-curve dynamics alongside achievement of the European Association of Urology Robotic Urology Section (ERUS) curriculum milestones [1]. The authors should be commended for providing long-term educational data in one of the most technically demanding procedures in urologic oncology and for demonstrating stable perioperative and oncologic outcomes during supervised trainee participation.

An important aspect that warrants further consideration is the use of operative-time–based cumulative sum analysis as the principal determinant of proficiency. Although operative time is a practical and widely used surrogate for technical efficiency, it may not fully capture surgical mastery in radical cystectomy, where quality metrics such as lymph node yield, adherence to extended pelvic lymph node dissection templates, urinary diversion reconstruction quality, and functional recovery are equally relevant. The study reports variability in the proportion of standard versus extended lymph node dissection across fellowship years, suggesting that procedural complexity may not have been uniform throughout the training period. Consequently, operative-time improvements may partially reflect case-mix variation rather than isolated gains in technical proficiency [2]. Incorporation of risk-adjusted learning models accounting for disease stage, body habitus, prior therapies, and extent of lymphadenectomy could provide a more precise assessment of competency acquisition.

The reported equivalence in oncologic outcomes between learning and proficiency phases is reassuring; however, interpretation is complicated by the substantial heterogeneity in baseline disease characteristics over the 10-yr study period. The increasing proportion of patients with muscle-invasive disease and exposure to neoadjuvant chemotherapy suggests evolving patient selection and treatment paradigms [3]. Given that cancer-specific survival was used as a surrogate for training safety, residual confounding from tumor biology and treatment evolution may influence observed outcomes independently of surgical performance. A competing-risk framework or multivariable survival analysis incorporating pathological stage, nodal burden, margin status, and neoadjuvant treatment exposure would strengthen the conclusion that trainee involvement does not adversely affect oncologic effectiveness [4]. Such analyses would also help determine whether educational progression remains robust across increasingly complex oncologic presentations.

Another noteworthy finding is the high rate of postfellowship independent robotic practice and self-reported achievement of ERUS curriculum milestones. While encouraging, competency assessment relied heavily on perceived proficiency. Educational literature has repeatedly demonstrated discordance between self-assessment and objectively measured technical performance. The inclusion of validated performance assessment tools, structured video review, procedure-specific objective metrics, or independent expert evaluation could further substantiate the relationship between curriculum completion and genuine operative competence [5]. Establishing objective competency benchmarks is particularly important as robotic fellowship models become increasingly adopted internationally and may influence credentialing standards.

Despite these considerations, Del Giudice and colleagues provide valuable evidence supporting structured, high-volume fellowship training for RARC with intracorporeal urinary diversion. Their findings contribute meaningfully to ongoing efforts aimed at standardizing robotic surgical education and offer an important framework for future competency-based training models that integrate technical performance, oncologic quality, and objective assessment strategies.



Conflicts of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.



Declaration of generative AI and AI-assisted technologies in the writing process: During the preparation of this work the authors used Grammarly and ChatGPT in order to refine language, grammar, and style. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.